ArticleMolecular & cellular proteomics : MCP2016
Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways.
Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Transcriptomic Insights into Lobular Breast Cancer Biology and Patient Outcomes: Analysis of the MINDACT Clinical Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- High Expression of PAPP-A Predicts Poor Outcomes in Oestrogen Receptor-Positive Breast Cancer Patients.Cancer medicine · 2026Article
- E-Cadherin-Mediated Cell-Cell Adhesion and Invasive Lobular Breast Cancer.Advances in experimental medicine and biology · 2025Review
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.Frontiers in systems biology · 2023Article
- Loss of E-cadherin Induces IGF1R Activation and Reveals a Targetable Pathway in Invasive Lobular Breast Carcinoma.Molecular cancer research : MCR · 2022Article
- Breast tumor IGF1R regulates cell adhesion and metastasis: alignment of mouse single cell and human breast cancer transcriptomics.Frontiers in oncology · 2022Article
- Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells.PLoS computational biology · 2021Article
- Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.Omics : a journal of integrative biology · 2020Review
- Biomarkers of Insulin and the Insulin-Like Growth Factor Axis in Relation to Breast Cancer Risk in Chinese Women.OncoTargets and therapy · 2020Article
- Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2019Article
- Loss of E-cadherin Enhances IGF1-IGF1R Pathway Activation and Sensitizes Breast Cancers to Anti-IGF1R/InsR Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- Protein network construction using reverse phase protein array data.Methods (San Diego, Calif.) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.